AI-Based Fraud Detection System for Financial Transactions

P. Muntaj Begum, G. Lilly, K. Madhavi, B. Sabeena, S. Likhitha

2025

Abstract

In the rapidly evolving digital financial ecosystem, fraud detection remains a critical challenge due to the increasing sophistication of fraudulent activities. This paper introduces Fraud Guard, an AI-based system created to detect and prevent deceptive financial transactions. using machine learning techniques. The system leverages Random Forest, Decision Tree, and Logistic Regression to analyze credit card transaction data, ensuring high precision and recall in fraud detection. Advanced feature engineering, anomaly detection, and external data sources like IP geolocation enhance its effectiveness. To solve key challenges, including imbalanced datasets, real-time detection, and adaptive learning, the system integrates data analytics, artificial intelligence, and deep learning to identify suspicious patterns while minimizing false positives. Fraud Guard continuously evolves to detect emerging fraud trends, ensuring robust and scalable fraud prevention. By implementing this AI-powered approach, financial institutions can enhance transaction security, safeguard customer funds, and mitigate financial fraud risks, including Various types of fraud, including credit card fraud, insurance fraud, securities fraud, insider trading, and money laundering.

Paper Citation

in Harvard Style

Begum P., Lilly G., Madhavi K., Sabeena B. and Likhitha S. (2025). AI-Based Fraud Detection System for Financial Transactions. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 421-428. DOI: 10.5220/0013914200004919

in Bibtex Style

@conference{icrdicct`2525,
author={P. Begum and G. Lilly and K. Madhavi and B. Sabeena and S. Likhitha},
title={AI-Based Fraud Detection System for Financial Transactions},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={421-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013914200004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - AI-Based Fraud Detection System for Financial Transactions
SN - 978-989-758-777-1
AU - Begum P.
AU - Lilly G.
AU - Madhavi K.
AU - Sabeena B.
AU - Likhitha S.
PY - 2025
SP - 421
EP - 428
DO - 10.5220/0013914200004919
PB - SciTePress